(1) Field of Invention
The present invention relates to a system for blind source separation and, more particularly, to a system for blind source separation with low complexity and memory requirements.
(2) Description of Related Art
State-of-the-art systems for detecting, localizing, and classifying source emitters from passive radio frequency (RF) antennas over an ultra-wide bandwidth (>30 Gigahertz (Gz)) require high rate analog-to-digital converters (ADC). Such high-rate ADCs are expensive and power hungry, and due to fundamental physical limits (such as the Walden curve described in Literature Reference No. 5 in the List of Incorporated Literature References), are not capable of achieving the sampling rate needed to capture the ultra-wide bandwidth. To mitigate this, current electronic support measures (ESM) systems use either spectrum sweeping (which is too slow to handle agile emitters) or a suite of digital channelizers, which have large size, weight, and power requirements. In addition, the detection, localization, and classification algorithm that ESM systems use are typically based on the fast Fourier transform, with high computational complexity and memory requirements that make it difficult to operate them in real-time over an ultra-wide bandwidth.
Conventional methods for blind source separation (BSS) typically require a greater number of input mixtures (which maps directly to a greater number of antenna) than the number of source signals, limiting their applicability in size, weight, and power (SWaP)-constrained scenarios (see Literature Reference No. 1). Some extensions to conventional BSS have addressed the “underdetermined” scenario (with fewer mixtures than sources) that leverage prior knowledge about the sources, such as having “low complexity” or having a sparse representation with respect to a learned dictionary. Such models of prior knowledge are too broad, enabling the system to overfit an entire mixture as a single source, and require large amounts of memory to store the dictionary and computation to recover the presentation of the input mixtures with respect to the dictionary (see Literature Reference Nos. 1 and 3.
In Literature Reference No. 2, the authors coupled the BSS algorithm with an infinite impulse response (IIR) bandpass filter with tunable center frequency in order to separate temporally correlated sources. Their work is limited, requiring at least as many mixtures as sources, requiring that the mixtures be “prewhitened” to have an identity-valued covariance matrix, and using the second-order statistics of sources as the sole cue for separation.
Thus, a continuing need exists for a system to separate multiple temporally correlated source signals over an ultra-wide bandwidth using as little as a single antenna.